afs_orig = read.csv("MGRB_GnomAD_SweGen_cancer_UKBB_AFs_outerjoined_ss-auto-gwas-snps_hcr.csv.xz", stringsAsFactors = FALSE, header = TRUE)
models_orig = read.csv("../data/manual_polygenic_scores.hcr_tag_rescued.csv", stringsAsFactors = FALSE, header = TRUE)
# Key allele frequencies by VID
afs_orig$vid = paste(afs_orig$chrom, afs_orig$pos, afs_orig$ref, afs_orig$alt, sep = ":")
afs_orig = afs_orig[,!(colnames(afs_orig) %in% c("chrom", "pos", "ref", "alt"))]
# Add gnomad AFs to the models for imputation of missing variants
temp.gnomad_af = (afs_orig$nAA_gnomad*2 + afs_orig$nRA_gnomad) / (2*(afs_orig$nAA_gnomad + afs_orig$nRA_gnomad + afs_orig$nRR_gnomad))
models_orig$aaf = temp.gnomad_af[match(models_orig$vid, afs_orig$vid)]
# Create a full UKBB cohort by combining the age-stratified numbers
afs_orig$nRR_ukbb = afs_orig$nRR_ukbb_0_55 + afs_orig$nRR_ukbb_55_60 + afs_orig$nRR_ukbb_60_65 + afs_orig$nRR_ukbb_65_70 + afs_orig$nRR_ukbb_70_75 + afs_orig$nRR_ukbb_75_inf
afs_orig$nRA_ukbb = afs_orig$nRA_ukbb_0_55 + afs_orig$nRA_ukbb_55_60 + afs_orig$nRA_ukbb_60_65 + afs_orig$nRA_ukbb_65_70 + afs_orig$nRA_ukbb_70_75 + afs_orig$nRA_ukbb_75_inf
afs_orig$nAA_ukbb = afs_orig$nAA_ukbb_0_55 + afs_orig$nAA_ukbb_55_60 + afs_orig$nAA_ukbb_60_65 + afs_orig$nAA_ukbb_65_70 + afs_orig$nAA_ukbb_70_75 + afs_orig$nAA_ukbb_75_inf
afs_orig$nmissing_ukbb = afs_orig$nmissing_ukbb_0_55 + afs_orig$nmissing_ukbb_55_60 + afs_orig$nmissing_ukbb_60_65 + afs_orig$nmissing_ukbb_65_70 + afs_orig$nmissing_ukbb_70_75 + afs_orig$nmissing_ukbb_75_inf
# Convert afs from wide to long format
library(reshape2)
afs_long = melt(afs_orig, id.vars = c("rsid", "negstrand", "vid"), value.name = "count")
afs_long$cohort = gsub("^n(RR|RA|AA|missing)_", "", afs_long$variable)
afs_long$variable = gsub("_.*", "", afs_long$variable)
afs = dcast(afs_long, vid + rsid + negstrand + cohort ~ variable, value.var = "count")
afs = afs[,c("vid", "rsid", "negstrand", "cohort", "nRR", "nRA", "nAA", "nmissing")]
# Exclude ASRB samples -- prelim examination suggests they are rather
# poor quality, and we are not interested in their PRS distributions
# anyway. Also exclude the various MGRB filtration options, as they
# apply only to rare variants. Exclude SweGen as we don't have a good
# HQ bed for it.
cohorts.sel = c("mgrborig", "gnomad", "ukbb")
cohorts.main = c("mgrborig", "gnomad", "ukbb")
afs = afs[afs$cohort %in% cohorts.sel,]
Choose polygenic models with at least 10 loci, with the exception of ShortLifespan:Deelen:10.1093/hmg/ddu139 (only six loci passing filters). For cancers, choose polygenic models only for cancers with a positive control cohort. In the case of multiple models for the same disorder, choose the most recent original publication where possible (ie exclude “meta” signatures if a good original report is available).
# Excluded due to insufficient size:
# "CancerOfBladder:Fritsche:10.1016/j.ajhg.2018.04.001",
# "LymphoidLeukemiaAcute:Fritsche:10.1016/j.ajhg.2018.04.001",
# "LymphoidLeukemiaChronic:Fritsche:10.1016/j.ajhg.2018.04.001",
# "MalignantNeoplasmOfTestis:Fritsche:10.1016/j.ajhg.2018.04.001",
# "NonHodgkinsLymphoma:Fritsche:10.1016/j.ajhg.2018.04.001",
# "PancreaticCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
# "APOE_rs429358:NA:NA",
# "ShortHealthspan:Zenin:10.1038/s42003-019-0290-0",
# "Deelen2019_90_disc",
# "Deelen2019_99_disc",
# Excluded because a better alternative was available
# "CancerOfProstate:Fritsche:10.1016/j.ajhg.2018.04.001",
# "ColorectalCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
# "MelanomasOfSkin:Fritsche:10.1016/j.ajhg.2018.04.001",
# "BreastCancer:Li:10.1038/gim.2016.43",
# "BreastCancerFemale:Fritsche:10.1016/j.ajhg.2018.04.001",
# "ShortParentalLifespan:Pilling:10.18632/aging.101334",
# Excluded because of issues with population-specific alleles between UK and European popns
# "BasalCellCarcinoma:Chahal:10.1038/ncomms12510",
# "BasalCellCarcinoma:Fritsche:10.1016/j.ajhg.2018.04.001",
# Excluded as superseded by Timmers / Pilling studies:
# "Deelen2014_85_rep",
# "Deelen2014_85_disc",
# "Deelen2014_90_rep",
# "Deelen2014_90_disc",
models.sel = c(
"AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143",
"DiastolicBP:Warren:10.1038/ng.3768",
"EOCAD:Theriault:10.1161/circgen.117.001849",
"PulsePressure:Warren:10.1038/ng.3768",
"SystolicBP:Warren:10.1038/ng.3768",
"AlzheimersDisease:Lambert:10.1038/ng.2802",
"ShortHealthspan:Zenin:10.1038/s42003-019-0290-0",
"ShortParentalLifespan:Timmers:10.7554/eLife.39856",
"Height:Wood:10.1038/ng.3097",
"BreastCancer:Michailidou:10.1038/nature24284",
"ColorectalCancer:Schumacher:10.1038/ncomms8138",
"Melanoma:Law:10.1038/ng.3373",
"ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315"
)
models = models_orig[models_orig$id %in% models.sel,]
# Drop variants with low genotyping rate in any cohort in which that
# variant was detected. Use the threshold of 97% genotyping rate
library(plyr)
temp.lowgt = ddply(afs[!is.na(afs$nRR),], .(cohort), function(d) mean(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing) >= 0.03))
temp.lowgt
## cohort V1
## 1 gnomad 1.596488e-03
## 2 mgrborig 8.066142e-05
## 3 ukbb 5.066883e-02
# The UKBB samples have rather a lot of dropouts here: ~ 5% of loci
# have a gt rate under 97%.
temp.gt_rate = 1 - daply(afs[!is.na(afs$nRR),], .(vid), function(d) {
d = d[d$cohort %in% cohorts.main,]
max(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing))})
mean(temp.gt_rate < 0.97)
## [1] 0.04621999
afs = afs[afs$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]
mean(models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97])
## [1] 0.9481481
# ~5.0% of model loci lost by this filter
models = models[models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]
# Create a set of AFs for variants that have VCF entries in every cohort.
# Note that given the relatively small size of some cohorts, this tends to
# preferentially exclude rare variants from consideration, and will probably
# attenuate the GRS differences.
afs.nmissing_per_cohort = tapply(is.na(afs$nRR[afs$cohort %in% cohorts.main]), afs$vid[afs$cohort %in% cohorts.main], sum)
afs.nomissing = afs[!(afs$vid %in% names(afs.nmissing_per_cohort[afs.nmissing_per_cohort > 0])),]
nrow(afs.nomissing) / nrow(afs)
## [1] 0.8785374
mean(models$vid %in% afs.nomissing$vid)
## [1] 0.671274
Here we examine all GWAS-reported loci that passed filtering.
temp.afs = afs.nomissing
temp.afs$fA = (temp.afs$nAA*2 + temp.afs$nRA) / (2*(temp.afs$nAA + temp.afs$nRA + temp.afs$nRR))
temp.afs = acast(temp.afs, vid ~ cohort, value.var = "fA", fill = NA)
pairs(temp.afs[,cohorts.main], pch = ".")
library(corrplot)
## corrplot 0.84 loaded
corrplot.mixed(cor(temp.afs[,cohorts.main]), lower = "ellipse", upper = "number")
temp.overall_cohort.pvals = sapply(cohorts.main[1:(length(cohorts.main)-1)], function(cohort_1) {
cohort_1_idx = which(cohorts.main == cohort_1)
afs_1 = temp.afs[,cohort_1]
sapply(cohorts.main[(cohort_1_idx+1):length(cohorts.main)], function(cohort_2) {
afs_2 = temp.afs[,cohort_2]
test = wilcox.test(afs_1 - afs_2)
test$p.value
})
})
temp.overall_cohort.pvals
## $mgrborig
## gnomad ukbb
## 7.208864e-01 2.784435e-17
##
## $gnomad
## ukbb
## 6.889826e-05
p.adjust(unlist(temp.overall_cohort.pvals), "holm")
## mgrborig.gnomad mgrborig.ukbb gnomad.ukbb
## 7.208864e-01 8.353305e-17 1.377965e-04
temp.af.gnomad = temp.afs[,"gnomad"]
temp.af.ukbb = temp.afs[,"ukbb"]
temp.af.mgrb = temp.afs[,"mgrborig"]
hist(temp.af.ukbb - temp.af.mgrb, breaks = c(-Inf, seq(-0.05, 0.05, 0.001), Inf), col = "grey", border = FALSE, xlim = c(-0.05, 0.05))
hist(temp.af.ukbb - temp.af.gnomad, breaks = c(-Inf, seq(-0.05, 0.05, 0.001), Inf), col = "grey", border = FALSE, xlim = c(-0.05, 0.05))
# Interestingly much higher AF diversity for gnomAD, despite it being a larger cohort. Population effects?
t.test(temp.af.ukbb - temp.af.mgrb)
##
## One Sample t-test
##
## data: temp.af.ukbb - temp.af.mgrb
## t = -7.2744, df = 21047, p-value = 3.603e-13
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.0004276082 -0.0002460822
## sample estimates:
## mean of x
## -0.0003368452
t.test(temp.af.ukbb - temp.af.gnomad)
##
## One Sample t-test
##
## data: temp.af.ukbb - temp.af.gnomad
## t = -4.7358, df = 21047, p-value = 2.196e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.0006723020 -0.0002786997
## sample estimates:
## mean of x
## -0.0004755008
There is a very slight but statistically significant skew in the AFs between UKBB and MGRB/gnomAD: UKBB has ever so slightly lower representation of the alt allele than both MGRB (0.0003360821 less) and gnomAD (0.0004744416 less). I suspect a subtle technical effect.
To address: is this of concern for the PRS calculations? This slight bias would be of concern if both:
2 is difficult to address before we calculate the actual PRS distributions. 1 however we can test now.
pheno_alleles = read.csv("../data/phenotype_associated_alleles.all.csv", stringsAsFactors = FALSE)
pheno_alleles = pheno_alleles[pheno_alleles$vid %in% afs.nomissing$vid,]
# Keep only alleles with consistent effect on a phenotype class
pheno_alleles = ddply(pheno_alleles, .(class, vid), function(d) {
if (nrow(d) > 1 && (all(d$direction == 1) || all(d$direction == -1)))
d = d[1,,drop=FALSE]
d
})
write.csv(pheno_alleles, "../data/phenotype_associated_alleles.filt.csv", quote = FALSE, row.names = FALSE)
pheno_alleles.tests = ddply(pheno_alleles, .(class), function(d) {
af.gnomad = temp.afs[d$vid, "gnomad"]
af.mgrb = temp.afs[d$vid, "mgrborig"]
af.ukbb = temp.afs[d$vid, "ukbb"]
daf.mgrb_gnomad = (af.mgrb - af.gnomad) * d$direction
daf.mgrb_ukbb = (af.mgrb - af.ukbb) * d$direction
n.mgrb_gt_gnomad.protective = sum(af.mgrb > af.gnomad & d$direction == -1)
n.mgrb_gt_gnomad.deleterious = sum(af.mgrb > af.gnomad & d$direction == 1)
n.gnomad_gt_mgrb.protective = sum(af.mgrb < af.gnomad & d$direction == -1)
n.gnomad_gt_mgrb.deleterious = sum(af.mgrb < af.gnomad & d$direction == 1)
ft.mgrb_gnomad = fisher.test(matrix(c(n.mgrb_gt_gnomad.protective, n.gnomad_gt_mgrb.protective, n.mgrb_gt_gnomad.deleterious, n.gnomad_gt_mgrb.deleterious), nrow = 2))
n.mgrb_gt_ukbb.protective = sum(af.mgrb > af.ukbb & d$direction == -1)
n.mgrb_gt_ukbb.deleterious = sum(af.mgrb > af.ukbb & d$direction == 1)
n.ukbb_gt_mgrb.protective = sum(af.mgrb < af.ukbb & d$direction == -1)
n.ukbb_gt_mgrb.deleterious = sum(af.mgrb < af.ukbb & d$direction == 1)
ft.mgrb_ukbb = fisher.test(matrix(c(n.mgrb_gt_ukbb.protective, n.ukbb_gt_mgrb.protective, n.mgrb_gt_ukbb.deleterious, n.ukbb_gt_mgrb.deleterious), nrow = 2))
data.frame(
class = d$class[[1]],
ref_cohort = "mgrb",
test_cohort = c("gnomad", "ukbb"),
deltaaf_direction.median = c(median(daf.mgrb_gnomad), median(daf.mgrb_ukbb)),
p.value.wilcox = c(wilcox.test(daf.mgrb_gnomad)$p.value, wilcox.test(daf.mgrb_ukbb)$p.value),
or.fisher = c(ft.mgrb_gnomad$estimate, ft.mgrb_ukbb$estimate),
cil.fisher = c(ft.mgrb_gnomad$conf.int[[1]], ft.mgrb_ukbb$conf.int[[1]]),
ciu.fisher = c(ft.mgrb_gnomad$conf.int[[2]], ft.mgrb_ukbb$conf.int[[2]]),
p.value.fisher = c(ft.mgrb_gnomad$p.value, ft.mgrb_ukbb$p.value))
})
pheno_alleles.tests$p.value.wilcox.holm = p.adjust(pheno_alleles.tests$p.value.wilcox, "holm")
pheno_alleles.tests$p.value.fisher.holm = p.adjust(pheno_alleles.tests$p.value.fisher, "holm")
pheno_alleles.tests
## class ref_cohort test_cohort deltaaf_direction.median
## 1 anthropometric mgrb gnomad -1.277496e-04
## 2 anthropometric mgrb ukbb -1.653798e-05
## 3 behavioural mgrb gnomad 2.728057e-05
## 4 behavioural mgrb ukbb -2.988239e-04
## 5 depleted mgrb gnomad -3.750209e-03
## 6 depleted mgrb ukbb -1.449596e-03
## p.value.wilcox or.fisher cil.fisher ciu.fisher p.value.fisher
## 1 3.668051e-01 1.1007675 0.7340203 1.653162 6.931847e-01
## 2 8.792646e-01 1.0006054 0.6673769 1.499636 1.000000e+00
## 3 6.599423e-01 0.9837027 0.6985942 1.384911 9.333614e-01
## 4 3.107888e-01 1.1153097 0.7921750 1.570790 5.573214e-01
## 5 3.717134e-10 2.6607300 1.9391732 3.662378 2.638859e-10
## 6 3.436825e-08 2.1320342 1.5567164 2.927677 1.050530e-06
## p.value.wilcox.holm p.value.fisher.holm
## 1 1.000000e+00 1.000000e+00
## 2 1.000000e+00 1.000000e+00
## 3 1.000000e+00 1.000000e+00
## 4 1.000000e+00 1.000000e+00
## 5 2.230280e-09 1.583315e-09
## 6 1.718413e-07 5.252648e-06
Save the allele freqs for a supp table
temp.nRR = acast(afs.nomissing, vid ~ cohort, value.var = "nRR", fill = NA)[,cohorts.main]
temp.nRA = acast(afs.nomissing, vid ~ cohort, value.var = "nRA", fill = NA)[,cohorts.main]
temp.nAA = acast(afs.nomissing, vid ~ cohort, value.var = "nAA", fill = NA)[,cohorts.main]
temp.nmissing = acast(afs.nomissing, vid ~ cohort, value.var = "nmissing", fill = NA)[,cohorts.main]
temp.AC = 2*temp.nAA + temp.nRA
temp.AN = 2*(temp.nRR + temp.nRA + temp.nAA)
temp = cbind(temp.AC, temp.AN)
colnames(temp)[1:ncol(temp.AC)] = paste("AC.", colnames(temp.AC), sep = "")
colnames(temp)[(ncol(temp.AC)+1):ncol(temp)] = paste("AN.", colnames(temp.AN), sep = "")
colnames(temp) = gsub("mgrborig", "mgrb", colnames(temp))
# Suppress generation of the file with UKBB data:
# write.csv(temp, file = "data/gwas_afs.csv", row.names = TRUE)
# We likely don't have permission to release UKBB AFs on such a broad scale, so
# generate a no UKBB set also:
write.csv(temp[,!grepl("ukbb", colnames(temp))], file = "gwas_afs_noukbb.csv", row.names = TRUE)
g.test = function(tbl)
{
expected = outer(rowSums(tbl), colSums(tbl)) / sum(tbl)
logoe = log(tbl/expected)
logoe[tbl == 0] = 0
stat = 2*sum(tbl*logoe)
pchisq(stat, prod(dim(tbl)-1), lower.tail = FALSE)
}
temp.locus_cohort.pvals = ddply(afs.nomissing[afs.nomissing$vid %in% models$vid & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
nR = d$nRR*2 + d$nRA
nA = d$nAA*2 + d$nRA
g.test(cbind(nR, nA))
# g.test(as.matrix(d[,c("nRR", "nRA", "nAA")]))
})
colnames(temp.locus_cohort.pvals)[2] = "p.value"
temp.locus_cohort.pvals$p.value.mtc = p.adjust(temp.locus_cohort.pvals$p.value, "BH")
# temp.locus_cohort.pvals$p.value.mtc is now calibrated for average false rejection rate
# (ie calling a SNP population-associated when it in fact isn't).
mean(temp.locus_cohort.pvals$p.value.mtc < 0.01)
## [1] 0.4721724
temp.locus_cohort.maxdeltaaf = ddply(afs.nomissing[afs.nomissing$vid %in% models$vid & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
nR = d$nRR*2 + d$nRA
nA = d$nAA*2 + d$nRA
AAF = nA/(nA+nR)
max(AAF) - min(AAF)
})
colnames(temp.locus_cohort.maxdeltaaf)[2] = "maxdeltaaaf"
temp.locus_cohort.maxdeltaaf[order(temp.locus_cohort.maxdeltaaf$maxdeltaaaf),]
## vid maxdeltaaaf
## 635 20:41851935:G:A 4.804901e-05
## 175 11:244552:A:G 5.472387e-04
## 698 3:163838015:A:C 5.854864e-04
## 848 5:176517326:T:C 6.520533e-04
## 865 5:58337481:T:G 6.896540e-04
## 581 2:232268312:T:C 8.648204e-04
## 64 1:242034263:A:G 8.926576e-04
## 561 2:19942473:G:A 8.944422e-04
## 800 4:61995613:A:G 9.804085e-04
## 344 15:32993111:C:T 1.013646e-03
## 126 10:12943973:C:T 1.036166e-03
## 431 17:43216281:C:T 1.120399e-03
## 940 6:43711981:T:C 1.242711e-03
## 87 1:65010606:T:G 1.410640e-03
## 1094 9:22062134:G:T 1.487932e-03
## 960 6:81792063:G:T 1.508864e-03
## 369 15:72842705:G:A 1.511067e-03
## 734 3:62133492:G:A 1.517305e-03
## 771 4:147993702:A:G 1.550150e-03
## 50 1:215046892:G:A 1.631525e-03
## 46 1:203766331:A:G 1.651584e-03
## 792 4:39503196:A:G 1.678110e-03
## 816 5:108625324:C:A 1.746323e-03
## 1095 9:22102165:C:T 1.753972e-03
## 35 1:17308254:T:C 1.809376e-03
## 465 18:19450303:A:G 1.867013e-03
## 463 17:800593:T:C 1.957503e-03
## 666 21:45867411:G:A 1.968592e-03
## 291 13:55934157:A:G 1.984521e-03
## 1114 9:99203606:T:C 2.015219e-03
## 547 2:179786068:T:C 2.214285e-03
## 222 12:125441159:T:C 2.246113e-03
## 365 15:67455630:C:T 2.312045e-03
## 447 17:64783539:C:T 2.316572e-03
## 1082 9:118826916:G:A 2.361107e-03
## 385 15:98615560:C:T 2.370182e-03
## 716 3:190815978:A:G 2.376253e-03
## 18 1:151259043:C:T 2.419978e-03
## 967 7:107259721:T:C 2.424953e-03
## 935 6:36659932:C:T 2.450101e-03
## 688 3:136107549:G:A 2.505036e-03
## 14 1:14105298:G:A 2.546910e-03
## 887 6:118569679:T:G 2.552647e-03
## 1011 8:110115372:C:T 2.598390e-03
## 158 11:11563879:C:T 2.615930e-03
## 301 13:80618435:A:G 2.616690e-03
## 982 7:148629759:C:T 2.621543e-03
## 719 3:27416013:C:T 2.622335e-03
## 552 2:191227755:A:G 2.638522e-03
## 480 18:57323149:C:A 2.650551e-03
## 1023 8:128106880:A:C 2.808417e-03
## 37 1:177279412:G:A 2.814433e-03
## 808 4:82204091:A:G 2.816117e-03
## 953 6:75452066:T:C 2.865091e-03
## 506 19:42683964:C:T 2.865981e-03
## 818 5:113748571:C:T 2.886389e-03
## 639 20:50141264:T:C 2.900362e-03
## 900 6:149608874:G:A 2.955662e-03
## 1013 8:115698881:G:A 2.962404e-03
## 485 18:77222862:T:G 2.971526e-03
## 333 14:90636206:G:A 3.008568e-03
## 210 12:112059557:C:T 3.037502e-03
## 577 2:219903723:C:T 3.051369e-03
## 655 21:27208935:G:T 3.062235e-03
## 843 5:171285632:C:T 3.075935e-03
## 612 2:56113538:G:A 3.098396e-03
## 829 5:141681788:G:A 3.104595e-03
## 1085 9:127900996:T:C 3.347011e-03
## 705 3:178467852:A:G 3.415386e-03
## 624 20:19465907:G:A 3.418120e-03
## 709 3:185313855:A:G 3.418663e-03
## 153 10:97805074:G:A 3.442529e-03
## 959 6:81038921:T:G 3.447571e-03
## 1012 8:110581794:A:G 3.470129e-03
## 180 11:45643843:C:T 3.483223e-03
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hist(temp.locus_cohort.maxdeltaaf$maxdeltaaaf)
# On the basis of this histogram, set a max delta aaf threshold of
# 4%. Drop loci with a delta AAF greater than this.
mean(temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04)
## [1] 0.9874327
temp.sel_loci = temp.locus_cohort.maxdeltaaf$vid[temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04]
afs = afs[afs$vid %in% temp.sel_loci,]
models = models[models$vid %in% temp.sel_loci,]
Attempt to model the cohort AAF differences.
temp.afs = afs
temp.afs$fA = (temp.afs$nAA*2 + temp.afs$nRA) / (2*(temp.afs$nAA + temp.afs$nRA + temp.afs$nRR))
temp.afs = acast(temp.afs, vid ~ cohort, value.var = "fA", fill = NA)
temp.af.gnomad = temp.afs[,"gnomad"]
temp.af.ukbb = temp.afs[,"ukbb"]
temp.af.mgrb = temp.afs[,"mgrborig"]
logit = function(p) log(p) - log(1-p)
pairs(cbind(mgrb = logit(temp.af.mgrb), ukbb = logit(temp.af.ukbb), delta = logit(temp.af.ukbb) - logit(temp.af.mgrb)), pch = ".")
pairs(cbind(mgrb = temp.af.mgrb, ukbb = temp.af.ukbb, delta = temp.af.ukbb - temp.af.mgrb), pch = ".")
pairs(cbind(mgrb = temp.af.mgrb, ukbb = temp.af.ukbb, deltastd = (temp.af.ukbb - temp.af.mgrb)/sqrt(temp.af.ukbb*(1-temp.af.ukbb))), pch = ".")
pairs(cbind(mgrb = logit(temp.af.mgrb), ukbb = logit(temp.af.ukbb), deltastd = (temp.af.ukbb - temp.af.mgrb)/sqrt(temp.af.ukbb*(1-temp.af.ukbb))), pch = ".")
hist((temp.af.ukbb - temp.af.mgrb)/sqrt(temp.af.mgrb*(1-temp.af.mgrb)), breaks = 25, col = "grey")
pairs(cbind(mgrb = logit(temp.af.mgrb), gnomad = logit(temp.af.gnomad), delta = logit(temp.af.gnomad) - logit(temp.af.mgrb)), pch = ".")
pairs(cbind(mgrb = temp.af.mgrb, gnomad = temp.af.gnomad, delta = temp.af.gnomad - temp.af.mgrb), pch = ".")
pairs(cbind(mgrb = temp.af.mgrb, gnomad = temp.af.gnomad, deltastd = (temp.af.gnomad - temp.af.mgrb)/sqrt(temp.af.gnomad*(1-temp.af.gnomad))), pch = ".")
pairs(cbind(mgrb = logit(temp.af.mgrb), gnomad = logit(temp.af.gnomad), deltastd = (temp.af.gnomad - temp.af.mgrb)/sqrt(temp.af.gnomad*(1-temp.af.gnomad))), pch = ".")
hist((temp.af.gnomad - temp.af.mgrb)/sqrt(temp.af.mgrb*(1-temp.af.mgrb)), breaks = 25, col = "grey")
# Looks like a sd norm makes the allele frequency difference largely independent of AAF.
# Therefore we can characterise the distribution of this normalised frequency difference
# and sample from it without needing to condition on the allele frequency.
# With > 20k loci we may as well just bootstrap directly instead of fitting a distribution
# and sampling.
norm_delta_aaf.mgrb_ukbb = (temp.af.mgrb - temp.af.ukbb)/sqrt(temp.af.ukbb*(1-temp.af.ukbb))
norm_delta_aaf.mgrb_gnomad = (temp.af.mgrb - temp.af.gnomad)/sqrt(temp.af.gnomad*(1-temp.af.gnomad))
Perform a ‘stress test’ for the PRS as suggested by Greg. We wish to show here that the PRS differences between MGRB and UKBB, or MGRB and GnomAD, are unlikely to be simply due to drift. To do this, we simulate hypothetical MGRB cohorts derived from either UKBB or GnomAD, that are consistent with the drift-only hypothesis.
The model:
POPULATION: UK --drift & ethnicity--> Australian --selection--> Australian Wellderly
| | |
V V V
SAMPLE: UKBB Australian* MGRB
The ideal comparison is the Australian sample vs MGRB. However, this Australian sample is not available. We simulate it by derivation from the UKBB sample, based on the MGRB - UKBB allele frequencies, on the assumption that the distribution of MAF_MGRB - MAF_UKBB will be very close to the distribution of MAF_Aus - MAF_UKBB. This is reasonable if we suppose that relatively few loci are linked to depleted MGRB phenotypes.
Proceed as follows:
stressboot.nboot = 100000
stressboot.cohorts = c("ukbb", "gnomad")
temp.afs.nomissing.modelonly = afs.nomissing[afs.nomissing$vid %in% models$vid,]
temp.afs.nomissing.modelonly$aaf = (temp.afs.nomissing.modelonly$nAA + 0.5*temp.afs.nomissing.modelonly$nRA)/(temp.afs.nomissing.modelonly$nAA + temp.afs.nomissing.modelonly$nRA + temp.afs.nomissing.modelonly$nRR)
stressboot.afmat.orig = acast(temp.afs.nomissing.modelonly, vid ~ cohort, value.var = "aaf")
stressboot.afmat.orig = stressboot.afmat.orig[,c("mgrborig", stressboot.cohorts)]
stressboot.models = acast(models, id ~ vid, value.var = "coef")
stressboot.models[is.na(stressboot.models)] = 0
stressboot.models = stressboot.models[,rownames(stressboot.afmat.orig)]
stopifnot(rownames(stressboot.afmat.orig) == colnames(stressboot.models))
stressboot.scores.orig = (2 * stressboot.models %*% stressboot.afmat.orig) / rowSums(stressboot.models != 0)
names(dimnames(stressboot.scores.orig)) = c("model", "cohort")
stressboot.normafdelta = list("aus_from_ukbb" = norm_delta_aaf.mgrb_ukbb, "aus_from_gnomad" = norm_delta_aaf.mgrb_gnomad)
stressboot.tasks = expand.grid(bootiter = 1:stressboot.nboot, deriv_cohort = paste("aus_from_", stressboot.cohorts, sep = ""))
stressboot.pb = progress_estimated(nrow(stressboot.tasks))
stressboot.scores.boot = aperm(daply(stressboot.tasks, .(deriv_cohort, bootiter), function(d) {
update_progress(stressboot.pb)
stopifnot(nrow(d) == 1)
source_cohort = gsub("^aus_from_", "", d$deriv_cohort)
bootiter = d$bootiter
this.normdelta = stressboot.normafdelta[[d$deriv_cohort]]
set.seed(314159+bootiter-1) # Will --> linked normdelta samples between the cohorts
this.delta = sample(this.normdelta, nrow(stressboot.afmat.orig), replace = TRUE)
this.aus_sim_af = stressboot.afmat.orig[,source_cohort] + this.delta*sqrt(stressboot.afmat.orig[,source_cohort]*(1-stressboot.afmat.orig[,source_cohort]))
this.aus_sim_af = pmax(0, pmin(1, this.aus_sim_af))
(2 * stressboot.models %*% this.aus_sim_af) / rowSums(stressboot.models != 0)
}), c(3, 1, 2))
names(dimnames(stressboot.scores.boot))[1] = "model"
saveRDS(stressboot.afmat.orig, "stressboot_afmat_orig.rds")
saveRDS(stressboot.models, "stressboot_models.rds")
print(rowSums(stressboot.models != 0))
## AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143
## 390
## AlzheimersDisease:Lambert:10.1038/ng.2802
## 12
## BreastCancer:Michailidou:10.1038/nature24284
## 64
## ColorectalCancer:Schumacher:10.1038/ncomms8138
## 35
## DiastolicBP:Warren:10.1038/ng.3768
## 28
## EOCAD:Theriault:10.1161/circgen.117.001849
## 93
## Height:Wood:10.1038/ng.3097
## 408
## Melanoma:Law:10.1038/ng.3373
## 11
## ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315
## 17
## PulsePressure:Warren:10.1038/ng.3768
## 21
## ShortHealthspan:Zenin:10.1038/s42003-019-0290-0
## 3
## ShortParentalLifespan:Timmers:10.7554/eLife.39856
## 7
## SystolicBP:Warren:10.1038/ng.3768
## 14
print(dim(stressboot.scores.orig))
## [1] 13 3
# Convert to data frames for ggplot
stressboot.dfs = list(original = melt(stressboot.scores.orig[,stressboot.cohorts]), boot = melt(stressboot.scores.boot))
temp.mgrb_scores = stressboot.scores.orig[,"mgrborig"]
temp.ukbb_sd = apply(stressboot.scores.boot[,"aus_from_ukbb",], 1, sd) # Get SDs from UKBB to normalise scores for plotting
stressboot.dfs$original$value.mgrb = temp.mgrb_scores[stressboot.dfs$original$model]
stressboot.dfs$boot$value.mgrb = temp.mgrb_scores[stressboot.dfs$boot$model]
stressboot.dfs$original$value.rel = stressboot.dfs$original$value - stressboot.dfs$original$value.mgrb
stressboot.dfs$boot$value.rel = stressboot.dfs$boot$value - stressboot.dfs$boot$value.mgrb
stressboot.dfs$summary = ddply(stressboot.dfs$boot, .(model, deriv_cohort), function(d) {
mean.rel = mean(d$value.rel)
ci.rel = quantile(d$value.rel, c(0.025, 0.975))
nneg = sum(d$value.rel < 0)
npos = sum(d$value.rel > 0)
p.value = 2 * (min(nneg, npos) + 0.5) / (nrow(d)+1)
c(p.value = p.value, mean.rel = mean.rel, lcl.rel = ci.rel[[1]], ucl.rel = ci.rel[[2]], nneg = nneg, npos = npos) })
stressboot.dfs$summary$p.value.holm = p.adjust(stressboot.dfs$summary$p.value, "holm")
stressboot.dfs$summary$p.value.BH = p.adjust(stressboot.dfs$summary$p.value, "BH")
stressboot.dfs$summary$mean.rel.norm = stressboot.dfs$summary$mean.rel / temp.ukbb_sd[stressboot.dfs$summary$model]
stressboot.dfs$summary$lcl.rel.norm = stressboot.dfs$summary$lcl.rel / temp.ukbb_sd[stressboot.dfs$summary$model]
stressboot.dfs$summary$ucl.rel.norm = stressboot.dfs$summary$ucl.rel / temp.ukbb_sd[stressboot.dfs$summary$model]
| model | deriv_cohort | p.value | mean.rel | lcl.rel | ucl.rel | nneg | npos | p.value.holm | p.value.BH | mean.rel.norm | lcl.rel.norm | ucl.rel.norm |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143 | aus_from_ukbb | 0.0254697 | 0.0001539 | 0.0000186 | 0.0002889 | 1273 | 98727 | 0.4584554 | 0.0704853 | 2.2313031 | 0.2700467 | 4.187125 |
| AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143 | aus_from_gnomad | 0.0000100 | 0.0006023 | 0.0003467 | 0.0008590 | 0 | 100000 | 0.0002600 | 0.0001300 | 8.7295956 | 5.0252141 | 12.450588 |
| AlzheimersDisease:Lambert:10.1038/ng.2802 | aus_from_ukbb | 0.0016300 | 0.0044430 | 0.0018189 | 0.0072322 | 81 | 99919 | 0.0342297 | 0.0061285 | 3.2416338 | 1.3270681 | 5.276685 |
| AlzheimersDisease:Lambert:10.1038/ng.2802 | aus_from_gnomad | 0.4454055 | 0.0019348 | -0.0029239 | 0.0068132 | 22270 | 77730 | 1.0000000 | 0.5514545 | 1.4116159 | -2.1333268 | 4.971015 |
| BreastCancer:Michailidou:10.1038/nature24284 | aus_from_ukbb | 0.1039290 | 0.0000973 | -0.0000205 | 0.0002144 | 5196 | 94804 | 1.0000000 | 0.2014051 | 1.6277837 | -0.3426078 | 3.584383 |
| BreastCancer:Michailidou:10.1038/nature24284 | aus_from_gnomad | 0.1482285 | 0.0001631 | -0.0000581 | 0.0003833 | 7411 | 92589 | 1.0000000 | 0.2408713 | 2.7279741 | -0.9708937 | 6.409991 |
| ColorectalCancer:Schumacher:10.1038/ncomms8138 | aus_from_ukbb | 0.1862281 | 0.0002565 | -0.0001251 | 0.0006365 | 9311 | 90689 | 1.0000000 | 0.2848195 | 1.3171974 | -0.6425888 | 3.268759 |
| ColorectalCancer:Schumacher:10.1038/ncomms8138 | aus_from_gnomad | 0.8344217 | 0.0000775 | -0.0006394 | 0.0007936 | 41721 | 58279 | 1.0000000 | 0.8865599 | 0.3977467 | -3.2835466 | 4.075410 |
| DiastolicBP:Warren:10.1038/ng.3768 | aus_from_ukbb | 0.0851491 | 0.0010263 | -0.0001432 | 0.0021916 | 4257 | 95743 | 1.0000000 | 0.1844898 | 1.7247084 | -0.2406805 | 3.682979 |
| DiastolicBP:Warren:10.1038/ng.3768 | aus_from_gnomad | 0.0001100 | 0.0043229 | 0.0021199 | 0.0065184 | 5 | 99995 | 0.0026400 | 0.0009533 | 7.2644743 | 3.5624394 | 10.954015 |
| EOCAD:Theriault:10.1161/circgen.117.001849 | aus_from_ukbb | 0.0005500 | 0.0003312 | 0.0001447 | 0.0005196 | 27 | 99973 | 0.0120999 | 0.0028600 | 3.4573950 | 1.5100011 | 5.424016 |
| EOCAD:Theriault:10.1161/circgen.117.001849 | aus_from_gnomad | 0.0002900 | 0.0006627 | 0.0003096 | 0.0010150 | 14 | 99986 | 0.0066699 | 0.0018850 | 6.9179573 | 3.2322752 | 10.595112 |
| Height:Wood:10.1038/ng.3097 | aus_from_ukbb | 0.1084489 | 0.0000344 | -0.0000077 | 0.0000763 | 5422 | 94578 | 1.0000000 | 0.2014051 | 1.6026750 | -0.3575338 | 3.560448 |
| Height:Wood:10.1038/ng.3097 | aus_from_gnomad | 0.0777692 | 0.0000712 | -0.0000080 | 0.0001504 | 3888 | 96112 | 1.0000000 | 0.1838182 | 3.3185787 | -0.3745018 | 7.012111 |
| Melanoma:Law:10.1038/ng.3373 | aus_from_ukbb | 0.8524615 | 0.0000875 | -0.0008361 | 0.0010104 | 42623 | 57377 | 1.0000000 | 0.8865599 | 0.1858653 | -1.7759509 | 2.146199 |
| Melanoma:Law:10.1038/ng.3373 | aus_from_gnomad | 0.9529005 | -0.0000534 | -0.0017990 | 0.0017017 | 52355 | 47645 | 1.0000000 | 0.9529005 | -0.1134544 | -3.8212453 | 3.614608 |
| ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315 | aus_from_ukbb | 0.0029300 | 0.0007735 | 0.0002657 | 0.0012748 | 146 | 99854 | 0.0556694 | 0.0095224 | 3.0000430 | 1.0305161 | 4.944542 |
| ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315 | aus_from_gnomad | 0.1331687 | 0.0007227 | -0.0002199 | 0.0016659 | 6658 | 93342 | 1.0000000 | 0.2308257 | 2.8030143 | -0.8531019 | 6.461811 |
| PulsePressure:Warren:10.1038/ng.3768 | aus_from_ukbb | 0.3382066 | 0.0007900 | -0.0008370 | 0.0024038 | 16910 | 83090 | 1.0000000 | 0.4628091 | 0.9574243 | -1.0144417 | 2.913353 |
| PulsePressure:Warren:10.1038/ng.3768 | aus_from_gnomad | 0.6848232 | 0.0006364 | -0.0024303 | 0.0037069 | 34241 | 65759 | 1.0000000 | 0.7880521 | 0.7712948 | -2.9454698 | 4.492809 |
| ShortHealthspan:Zenin:10.1038/s42003-019-0290-0 | aus_from_ukbb | 0.3228668 | 0.0004000 | -0.0003925 | 0.0012052 | 16143 | 83857 | 1.0000000 | 0.4628091 | 0.9852262 | -0.9667595 | 2.968370 |
| ShortHealthspan:Zenin:10.1038/s42003-019-0290-0 | aus_from_gnomad | 0.6971230 | -0.0002971 | -0.0017708 | 0.0011913 | 65144 | 34856 | 1.0000000 | 0.7880521 | -0.7317064 | -4.3615137 | 2.934141 |
| ShortParentalLifespan:Timmers:10.7554/eLife.39856 | aus_from_ukbb | 0.0000100 | 0.0129934 | 0.0075328 | 0.0185327 | 0 | 100000 | 0.0002600 | 0.0001300 | 4.6370240 | 2.6882574 | 6.613880 |
| ShortParentalLifespan:Timmers:10.7554/eLife.39856 | aus_from_gnomad | 0.0271097 | 0.0112661 | 0.0011758 | 0.0214100 | 1355 | 98645 | 0.4608654 | 0.0704853 | 4.0205958 | 0.4196148 | 7.640704 |
| SystolicBP:Warren:10.1038/ng.3768 | aus_from_ukbb | 0.0016500 | 0.0045264 | 0.0016972 | 0.0073337 | 82 | 99918 | 0.0342297 | 0.0061285 | 3.1430432 | 1.1785241 | 5.092330 |
| SystolicBP:Warren:10.1038/ng.3768 | aus_from_gnomad | 0.4281457 | 0.0021495 | -0.0031346 | 0.0074542 | 21407 | 78593 | 1.0000000 | 0.5514545 | 1.4925387 | -2.1765863 | 5.176041 |
stressboot.dfs$summary$plot_cohort = c("aus_from_ukbb" = "UKBB", "aus_from_gnomad" = "gnomAD")[stressboot.dfs$summary$deriv_cohort]
library(ggplot2)
ggplot(stressboot.dfs$summary, aes(x = model, y = mean.rel.norm, ymin = lcl.rel.norm, ymax = ucl.rel.norm, col = plot_cohort, group = plot_cohort)) +
geom_point(position = position_dodge(width = 0.7), size = 2) + geom_errorbar(position = position_dodge(width = 0.7), width = 0.3, lwd = 1) +
geom_hline(yintercept = 0, lty = "dashed") +
labs(y = "Polygenic score relative to MGRB, normalised", x = "Polygenic model", col = "Comparison cohort") + theme_bw() + coord_flip()
for (i in 1:nrow(stressboot.dfs$summary))
{
try(
dafplot(
afs1 = afs.nomissing[afs.nomissing$cohort == "mgrborig",],
afs2 = afs.nomissing[afs.nomissing$cohort == gsub("^aus_from_", "", stressboot.dfs$summary$deriv_cohort[i]),],
model = models[models$id == stressboot.dfs$summary$model[i],],
main = stressboot.dfs$summary$model[i],
xlab = "",
# xlab = expression(paste("PRS ", beta)),
ylab = sprintf("MAF MGRB - %s", gsub("^aus_from_", "", stressboot.dfs$summary$deriv_cohort[i])),
mar = c(9, 4, 4, 2)+0.1,
sub = sprintf("\ndelta_mean=%.3e (%.3e-%.3e)\np.raw=%.4f p.holm=%.4f p.bh=%.4f", stressboot.dfs$summary$mean.rel[i], stressboot.dfs$summary$lcl.rel[i], stressboot.dfs$summary$ucl.rel[i], stressboot.dfs$summary$p.value[i], stressboot.dfs$summary$p.value.holm[i], stressboot.dfs$summary$p.value.BH[i]))
)
}
sessionInfo()
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.1.1 kableExtra_1.1.0 knitr_1.22
## [4] knitrProgressBar_1.1.0 corrplot_0.84 plyr_1.8.4
## [7] reshape2_1.4.3 rmarkdown_1.12 RevoUtils_11.0.3
## [10] RevoUtilsMath_11.0.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 highr_0.8 compiler_3.5.3
## [4] pillar_1.3.1 R.methodsS3_1.7.1 tools_3.5.3
## [7] digest_0.6.18 gtable_0.3.0 evaluate_0.13
## [10] tibble_2.1.1 viridisLite_0.3.0 pkgconfig_2.0.2
## [13] rlang_0.3.4 rstudioapi_0.10 yaml_2.2.0
## [16] xfun_0.6 withr_2.1.2 dplyr_0.8.0.1
## [19] stringr_1.4.0 httr_1.4.0 xml2_1.2.0
## [22] hms_0.4.2 tidyselect_0.2.5 grid_3.5.3
## [25] webshot_0.5.1 glue_1.3.1 R6_2.3.0
## [28] purrr_0.3.2 readr_1.3.1 magrittr_1.5
## [31] scales_1.0.0 htmltools_0.3.6 assertthat_0.2.1
## [34] rvest_0.3.3 colorspace_1.4-1 labeling_0.3
## [37] stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0
## [40] crayon_1.3.4 R.oo_1.22.0